Load libraries and data

easypackages::libraries("here","ggplot2","caret","e1071","pheatmap","reshape2","NbClust","grid","patchwork","readxl","patchwork","WGCNA","psych","nlme")
source(here("code","ndar_functions.R"))
source(here("code","euaims_functions.R"))
source(here("code","get_ggColorHue.R"))

options(stringsAsFactors = FALSE)

# Z-score threshold to use for subtyping
z_thresh = 0.6

codepath = here("code")
datapath = here("data")
figpath = here("figures")
resultpath = here("results","ndar")
plotpath = here("plots","ndar")

# read in data
Dverbal_Discovery = read.csv(file.path(datapath,"tidy_verbal_disc.csv"))
Dverbal_Replication = read.csv(file.path(datapath,"tidy_verbal_rep.csv"))

Subtyping using Z-score of the difference between SC and RRB

make_subtype <- function(data2use, z_thresh, mean2use=NULL, sd2use=NULL){
  # compute difference score
  vars2use = c("dbaes_atotal","dbaes_btotal")
  diff_score = data2use[,vars2use[1]] - data2use[,vars2use[2]]
  
  # compute mean and sd if necessary
  if (is.null(mean2use)){
    mean2use = mean(diff_score)
  } # if (is.null(mean2use))
  
  if (is.null(sd2use)){
    sd2use = sd(diff_score)
  } # if (is.null(sd2use))
  
  # compute z-score
  data2use$z_ds = (diff_score - mean2use)/sd2use
  
  # make subtype factor
  data2use$z_ds_group = "SC_equal_RRB"
  data2use$z_ds_group[data2use$z_ds>z_thresh] = "SC_over_RRB"
  data2use$z_ds_group[data2use$z_ds<(z_thresh*-1)] = "RRB_over_SC"
  data2use$z_ds_group = factor(data2use$z_ds_group)
  return(data2use)
  
} # function make_subtype

vars2use = c("dbaes_atotal","dbaes_btotal")

# compute Discovery mean and sd
ds_disc = Dverbal_Discovery[,vars2use[1]] - Dverbal_Discovery[,vars2use[2]]
mean2use = mean(ds_disc) 
sd2use = sd(ds_disc)

Dverbal_Discovery = make_subtype(data2use = Dverbal_Discovery,
                                 z_thresh = z_thresh,
                                 mean2use = mean2use,
                                 sd2use = sd2use)

# compute Replication mean and sd
ds_rep = Dverbal_Replication[,vars2use[1]] - Dverbal_Replication[,vars2use[2]]
# mean2use = mean(ds_rep) 
# sd2use = sd(ds_rep)

Dverbal_Replication = make_subtype(data2use = Dverbal_Replication,
                                 z_thresh = z_thresh,
                                 mean2use = mean2use,
                                 sd2use = sd2use)

Make scatterplots with difference score Z subtypes in different colors

maxScores = c(3,4)

p_disc = ggplot(data = Dverbal_Discovery, aes(x = dbaes_atotal, y = dbaes_btotal, colour = z_ds_group)) + geom_point() + xlab("SC") + ylab("RRB") + ylim(0,1) + xlim(0,1) + ggtitle("NDAR Discovery")
p1_top_left = p_disc + guides(colour=FALSE)
ggsave(filename = file.path(plotpath, sprintf("final_NDAR_Disc_scatterplot_z%s.pdf",as.character(z_thresh))), plot = p1_top_left)
p_disc

table(Dverbal_Discovery$z_ds_group)
## 
##  RRB_over_SC SC_equal_RRB  SC_over_RRB 
##          248          405          236
cor_res = cor.test(Dverbal_Discovery$dbaes_atotal, Dverbal_Discovery$dbaes_btotal)
cor_res
## 
##  Pearson's product-moment correlation
## 
## data:  Dverbal_Discovery$dbaes_atotal and Dverbal_Discovery$dbaes_btotal
## t = 6.6829, df = 887, p-value = 4.134e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1554332 0.2806578
## sample estimates:
##       cor 
## 0.2189469
p_rep = ggplot(data = Dverbal_Replication, aes(x = dbaes_atotal, y = dbaes_btotal, colour = z_ds_group)) + geom_point() + xlab("SC") + ylab("RRB") + ylim(0,1) + xlim(0,1) +  ggtitle("NDAR Replication")
p2_bottom_left = p_rep + guides(colour=FALSE)
ggsave(filename = file.path(plotpath, sprintf("final_NDAR_Rep_scatterplot_z%s.pdf",as.character(z_thresh))), plot = p2_bottom_left)
p_rep

table(Dverbal_Replication$z_ds_group)
## 
##  RRB_over_SC SC_equal_RRB  SC_over_RRB 
##          250          420          220
cor_res = cor.test(Dverbal_Replication$dbaes_atotal, Dverbal_Replication$dbaes_btotal)
cor_res
## 
##  Pearson's product-moment correlation
## 
## data:  Dverbal_Replication$dbaes_atotal and Dverbal_Replication$dbaes_btotal
## t = 7.1459, df = 888, p-value = 1.864e-12
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1700824 0.2943934
## sample estimates:
##       cor 
## 0.2331903

Run supervised model with Discovery as training and Replication as Test

# run validation
# make subtypes using z-scores computed from the mean and sd of the training set
train_data = Dverbal_Discovery
test_data = Dverbal_Replication

mean2use = mean(train_data[,vars2use[1]] - train_data[,vars2use[2]])
sd2use = sd(train_data[,vars2use[1]] - train_data[,vars2use[2]])
tmp_train = make_subtype(data2use = train_data,
                         z_thresh = z_thresh,
                         mean2use = mean2use,
                         sd2use = sd2use)

# mean2use = mean(test_data[,vars2use[1]] - test_data[,vars2use[2]])
# sd2use = sd(test_data[,vars2use[1]] - test_data[,vars2use[2]])
tmp_test = make_subtype(data2use = test_data,
                        z_thresh = z_thresh,
                        mean2use = mean2use,
                        sd2use = sd2use)
# compute model
mod2use = svm(x = tmp_train[,vars2use], y = tmp_train$z_ds_group)
pred_labels = predict(mod2use, tmp_test[,vars2use])
confmat = table(tmp_test$z_ds_group,pred_labels)
acc = (confmat[1,1]+confmat[2,2]+confmat[3,3])/length(pred_labels)

# plot confusion matrix
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.9, name="vp", just=c("right","top"))), action="prepend")
pheatmap(confmat/rowSums(confmat)*100, display_numbers = TRUE, color = colorRampPalette(c('white','red'))(100), cluster_rows = FALSE, cluster_cols = FALSE, fontsize_number = 15,labels_row = c("RRB>SC","SC=RRB","SC>RRB"),labels_col = c("RRB>SC","SC=RRB","SC>RRB"),angle_col=90)
setHook("grid.newpage", NULL, "replace")
grid::grid.text("Actual Labels", y=-0.07, gp=gpar(fontsize=16))
grid::grid.text("Predicted Labels", x=-0.07, rot=90, gp=gpar(fontsize=16))

# show accuracy
true_accuracy = acc
true_accuracy
## [1] 0.9786517

Permute subtype labels to examine how well supervised model performs

nperm = 10000
# make subtypes using z-scores computed from the mean and sd of the training set
train_data = Dverbal_Discovery
test_data = Dverbal_Replication
mean2use = mean(train_data[,vars2use[1]] - train_data[,vars2use[2]])
sd2use = sd(train_data[,vars2use[1]] - train_data[,vars2use[2]])
tmp_train = make_subtype(data2use = train_data,
                         z_thresh = z_thresh,
                         mean2use = mean2use,
                         sd2use = sd2use)

# mean2use = mean(test_data[,vars2use[1]] - test_data[,vars2use[2]])
# sd2use = sd(test_data[,vars2use[1]] - test_data[,vars2use[2]])
tmp_test = make_subtype(data2use = test_data,
                        z_thresh = z_thresh,
                        mean2use = mean2use,
                        sd2use = sd2use)

acc = vector(length = nperm)
for (iperm in 1:nperm){
  # set seed for reproducibility
  set.seed(iperm)
  # compute model
  permuted_labels = sample(tmp_train$z_ds_group)
  mod2use = svm(x = tmp_train[,vars2use], y = permuted_labels)
  pred_labels = predict(mod2use, tmp_test[,vars2use])
  confmat = table(tmp_test$z_ds_group,pred_labels)
  acc[iperm] = (confmat[1,1]+confmat[2,2]+confmat[3,3])/length(pred_labels)
} # for (iperm in 1:nperm)

df2plot = data.frame(Accuracy = acc)
p = ggplot(data = df2plot, aes(x = Accuracy)) + geom_histogram() + geom_vline(xintercept=true_accuracy)
p

# compute p-value
pval = sum(c(true_accuracy,acc)>=true_accuracy)/(nperm+1)
pval
## [1] 9.999e-05

Plot difference score Z subtypes in Discovery set

maxScores = c(3,4)

# Discovery - make plot with all individuals shown as lines
df2use = Dverbal_Discovery[,c("subjectkey",adi_total_vars2use)]
df2use$subgrp = factor(tmp_train$z_ds_group)
df2use = data.frame(df2use)
df2use$subjectkey = factor(df2use$subjectkey)
df2use$SC = df2use$dbaes_atotal
df2use$RRB = df2use$dbaes_btotal

df4plot = melt(df2use,
               id.vars = c("subjectkey","subgrp"), 
               measure.vars = c("SC","RRB"))

p = ggplot(data = df4plot, aes(x = variable, 
                               y = value, 
                               colour = subgrp, 
                               group = subjectkey)) + facet_grid(. ~ subgrp)
p = p + geom_point(shape=1) + geom_line(alpha = 0.2) + ylim(0,1) + guides(color=FALSE)
p = p + ylab("Percent Severity") + xlab("ADI-R subscale")
p3_middle_top = p + guides(colour=FALSE)
ggsave(filename = file.path(plotpath, sprintf("final_NDAR_Disc_jitterplot_z%s.pdf",as.character(z_thresh))), plot = p3_middle_top)
p

Plot difference score Z subtypes in Replication set

maxScores = c(3,4)

# Replication - make plot with all individuals shown as lines
df2use = Dverbal_Replication[,c("subjectkey",adi_total_vars2use)]
df2use$subgrp = factor(tmp_test$z_ds_group)
df2use = data.frame(df2use)
df2use$subjectkey = factor(df2use$subjectkey)
df2use$SC = df2use$dbaes_atotal
df2use$RRB = df2use$dbaes_btotal

df4plot = melt(df2use,
               id.vars = c("subjectkey","subgrp"), 
               measure.vars = c("SC","RRB"))

p = ggplot(data = df4plot, aes(x = variable, 
                               y = value, 
                               colour = subgrp, 
                               group = subjectkey)) + facet_grid(. ~ subgrp)
p = p + geom_point(shape=1) + geom_line(alpha = 0.2) + ylim(0,1) + guides(color=FALSE)
p = p + ylab("Percent Severity") + xlab("ADI-R subscale")
p4_middle_bottom = p + guides(colour=FALSE)
ggsave(filename = file.path(plotpath, sprintf("final_NDAR_Rep_jitterplot_z%s.pdf",as.character(z_thresh))), plot = p4_middle_bottom)
p

Apply NDAR subtypes to EU-AIMS LEAP data

# make SC and RRB percentages since EU-AIMS data is specified as percentages
Dverbal = read.csv(file.path(datapath,"tidy_verbal.csv"))
euaims_data = read.csv(file.path(datapath,"tidy_euaims.csv"))
mask1 = euaims_data$Diagnosis=="ASD" 
mask2 =  (is.na(euaims_data$A1_pct_severity) | is.na(euaims_data$A2_pct_severity) | is.na(euaims_data$A3_pct_severity) | is.na(euaims_data$B1_pct_severity) | is.na(euaims_data$B2_pct_severity) | is.na(euaims_data$B3_pct_severity) | is.na(euaims_data$B4_pct_severity)) 
euaims_data = subset(euaims_data, (mask1 & !mask2))


Dverbal[,vars2use[1]] = Dverbal[,vars2use[1]]
Dverbal[,vars2use[2]] = Dverbal[,vars2use[2]]
euaims_data[,vars2use[1]] = (euaims_data$A1_pct_severity + 
                               euaims_data$A2_pct_severity + 
                               euaims_data$A3_pct_severity)/3
euaims_data[,vars2use[2]] = (euaims_data$B1_pct_severity + 
                               euaims_data$B2_pct_severity + 
                               euaims_data$B3_pct_severity + 
                               euaims_data$B4_pct_severity)/4

train_data = Dverbal
test_data = euaims_data

mean2use = mean(train_data[,vars2use[1]] - train_data[,vars2use[2]], na.rm=TRUE)
sd2use = sd(train_data[,vars2use[1]] - train_data[,vars2use[2]], na.rm=TRUE)
c(mean2use, sd2use)
## [1] -0.01045243  0.19482749
tmp_train = make_subtype(data2use = train_data,
                         z_thresh = z_thresh,
                         mean2use = mean2use,
                         sd2use = sd2use)

tmp_test = make_subtype(data2use = test_data,
                        z_thresh = z_thresh,
                        mean2use = mean2use,
                        sd2use = sd2use)

# compute model
mod2use = svm(x = tmp_train[,vars2use], y = tmp_train$z_ds_group)
pred_labels = predict(mod2use, tmp_test[,vars2use])
confmat = table(tmp_test$z_ds_group,pred_labels)
acc = (confmat[1,1]+confmat[2,2]+confmat[3,3])/length(pred_labels)

tmp_test$svm_pred_labels = pred_labels

# plot confusion matrix
setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.9, name="vp", just=c("right","top"))), action="prepend")
pheatmap(confmat/rowSums(confmat)*100, display_numbers = TRUE, color = colorRampPalette(c('white','red'))(100), cluster_rows = FALSE, cluster_cols = FALSE, fontsize_number = 15,labels_row = c("RRB>SC","SC=RRB","SC>RRB"),labels_col = c("RRB>SC","SC=RRB","SC>RRB"),angle_col=90)
setHook("grid.newpage", NULL, "replace")
grid::grid.text("Actual Labels", y=-0.07, gp=gpar(fontsize=16))
grid::grid.text("Predicted Labels", x=-0.07, rot=90, gp=gpar(fontsize=16))

# scatterplot
p1 = ggplot(data = tmp_train, aes(x = dbaes_atotal, y = dbaes_btotal, colour = factor(z_ds_group))) + geom_point() + xlab("Social-Communication") + ylab("Restricted Repetitive Behaviors") + ylim(0,0.8) + xlim(0,0.8) + ggtitle("NDAR ALL")
p1

p2 = ggplot(data = tmp_test, aes(x = dbaes_atotal, y = dbaes_btotal, colour = factor(z_ds_group))) + geom_point() + xlab("Social-Communication") + ylab("Restricted Repetitive Behaviors") + ylim(0,0.8) + xlim(0,0.8) + ggtitle("EU-AIMS with Groups from NDAR ALL")
p2

p3 = ggplot(data = tmp_test, aes(x = dbaes_atotal, y = dbaes_btotal, colour = factor(pred_labels))) + geom_point() + xlab("SC") + ylab("RRB") + ylim(0,0.8) + xlim(0,0.8) + ggtitle("EU-AIMS")
p5_bottom_right = p3 + guides(colour=FALSE)
ggsave(filename = file.path(plotpath, sprintf("final_EUAIMS_scatterplot_z%s.pdf",as.character(z_thresh))), plot = p5_bottom_right)
p3

# # write out EU-AIMS LEAP data with subgroups defined by NDAR All
write.csv(tmp_test, file.path(datapath, sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))

Make final plot

p_final = p1_top_left + p3_middle_top + plot_spacer() + p2_bottom_left + p4_middle_bottom + p5_bottom_right + plot_layout(nrow=3, ncol=3, widths = c(4,4,4), heights = c(8,8,8))
ggsave(filename = file.path(plotpath, sprintf("final_NDAR_EUAIMS_subtypes_plot_z%s.pdf",as.character(z_thresh))), plot = p_final)
p_final

Integrate subtypes with rest of EU-AIMS LEAP data

euaims_data = read.csv(file.path(datapath,"tidy_euaims.csv"))
td_df = subset(euaims_data, euaims_data$Diagnosis=="TD",select=2:6)
td_df$A_pct_severity = as.numeric(NA)
td_df$B_pct_severity = as.numeric(NA)
td_df$subgrp = "TD"

cols2use = c("subid","age","Centre","Schedule","Diagnosis","dbaes_atotal","dbaes_btotal","svm_pred_labels")
tmp_asd = tmp_test[,cols2use]
colnames(tmp_asd)[6] = "A_pct_severity"
colnames(tmp_asd)[7] = "B_pct_severity"
colnames(tmp_asd)[8] = "subgrp"
asd_df = tmp_asd

all_data = rbind(td_df,asd_df)

fname = "/Users/mlombardo/Dropbox/euaims/data/rsfmri_preproc/euaims_preproc.xlsx"
pp_data = read_excel(fname)
mask = pp_data$notes=="ok"
pp_data = subset(pp_data, mask)

asd_df = merge(pp_data[,c("subid","sex")],asd_df, by = "subid")
td_df = merge(pp_data[,c("subid","sex")],td_df, by = "subid")

all_data = rbind(td_df,asd_df)

data2write = merge(pp_data, all_data, by = "subid")
data2write$age = data2write$age.x
data2write$sex = data2write$sex.y
print(table(data2write$Schedule, data2write$subgrp))
##    
##     RRB_over_SC SC_equal_RRB SC_over_RRB TD
##   A           7           34          39 78
##   B           4           37          44 83
##   C           4           28          31 59
##   D           1            7          30 23
print(table(data2write$Centre, data2write$subgrp))
##                
##                 RRB_over_SC SC_equal_RRB SC_over_RRB TD
##   CAMBRIDGE               4           25          13 29
##   KINGS_COLLEGE           8           42          56 78
##   MANNHEIM                0            0           0 34
##   NIJMEGEN                4           33          56 64
##   UTRECHT                 0            6          19 38
print(table(data2write$sex, data2write$subgrp))
##         
##          RRB_over_SC SC_equal_RRB SC_over_RRB  TD
##   Female           6           27          38  88
##   Male            10           79         106 155
#DSM-5 - find best split that balances participants across sites
a = findBestSplit(asd_df, seed_range = c(172342)) #,300001:500000))
## [1] 172342
best_seeds = a$seed[!is.na(a$discrepancy) & a$discrepancy==min(a$discrepancy, na.rm = TRUE)]
print(best_seeds)
## [1] 172342
# Split datasets -------------------------------------------------------------
rngSeed = best_seeds[1]

# split Schedule A dataset
dset2use = subset(asd_df, asd_df$Schedule=="A")
tmp_d = SplitDatasetsBySex(dset2use, rngSeed = rngSeed)
A_Discovery = tmp_d[[2]]
A_Replication = tmp_d[[1]]

# split Schedule B dataset
dset2use = subset(asd_df, asd_df$Schedule=="B")
tmp_d = SplitDatasetsBySex(dset2use, rngSeed = rngSeed)
B_Discovery = tmp_d[[2]]
B_Replication = tmp_d[[1]]

# split Schedule C dataset
dset2use = subset(asd_df, asd_df$Schedule=="C")
tmp_d = SplitDatasetsBySex(dset2use, rngSeed = rngSeed)
C_Discovery = tmp_d[[2]]
C_Replication = tmp_d[[1]]

# split Schedule D dataset
dset2use = subset(asd_df, asd_df$Schedule=="D")
tmp_d = SplitDatasetsBySex(dset2use, rngSeed = rngSeed)
D_Discovery = tmp_d[[2]]
D_Replication = tmp_d[[1]]

df_Disc = rbind(A_Discovery, B_Discovery, C_Discovery, D_Discovery)
df_Rep = rbind(A_Replication, B_Replication, C_Replication, D_Replication)

a = table(df_Disc$Schedule, df_Disc$Centre); print(a)
##    
##     CAMBRIDGE KINGS_COLLEGE NIJMEGEN UTRECHT
##   A         6            17       10       7
##   B         9            16       14       3
##   C         6             9       14       2
##   D         0            10       10       0
b = table(df_Rep$Schedule, df_Rep$Centre); print(b)
##    
##     CAMBRIDGE KINGS_COLLEGE NIJMEGEN UTRECHT
##   A         7            18       10       5
##   B         8            17       13       5
##   C         6            10       13       3
##   D         0             9        9       0
print(a-b)
##    
##     CAMBRIDGE KINGS_COLLEGE NIJMEGEN UTRECHT
##   A        -1            -1        0       2
##   B         1            -1        1      -2
##   C         0            -1        1      -1
##   D         0             1        1       0
print(sum(rowSums(abs(a-b))))
## [1] 14
data2write$dataset = NA
mask  = is.element(data2write$subid,df_Disc$subid)
data2write[mask,"dataset"] = "Discovery"
mask  = is.element(data2write$subid,df_Rep$subid)
data2write[mask,"dataset"] = "Replication"

asd_Disc = subset(data2write, data2write$dataset=="Discovery" & data2write$Diagnosis=="ASD")
asd_Rep = subset(data2write, data2write$dataset=="Replication" & data2write$Diagnosis=="ASD")

# find which seed gives best TD age-match -------------------------------------
seeds = 1:1000
pvals = data.frame(matrix(nrow = length(seeds), ncol = 2))
for (i in 1:length(seeds)) {
  res = findTDAgeMatch(data2write, seed_range = c(seeds[i],seeds[i]))
  td_Disc_matched = res[[2]]
  td_Rep_matched = res[[1]]
  tres = t.test(td_Disc_matched$age, asd_Disc$age)
  pvals[i,1] = tres$p.value
  tres = t.test(td_Rep_matched$age, asd_Rep$age)
  pvals[i,2] = tres$p.value
  #print(i)
}
a = sort.int(pvals[,1], decreasing = TRUE, index.return = TRUE)
b=pvals[a$ix,]

seed2use = 929
res = findTDAgeMatch(data2write, seed_range = c(seed2use,seed2use))
td_Disc_matched = res[[2]]
td_Rep_matched = res[[1]]

mask = is.element(data2write$subid, td_Disc_matched$subid)
data2write$dataset[mask] = "Discovery"

mask = is.element(data2write$subid, td_Rep_matched$subid)
data2write$dataset[mask] = "Replication"

print(table(data2write$dataset, data2write$subgrp))
##              
##               RRB_over_SC SC_equal_RRB SC_over_RRB  TD
##   Discovery            11           52          70 121
##   Replication           5           54          74 122
fname2save = here(sprintf("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
write.csv(data2write,file = fname2save)

Plot mean FD and hypothesis test

df2use = subset(data2write, data2write$subgrp!="RRB_over_SC")

p = ggplot(data = df2use, aes(x = subgrp, y =meanFD, colour = subgrp)) + facet_wrap(. ~ dataset)
p = p + geom_jitter() + geom_boxplot(fill = NA, colour = "#000000", outlier.shape = NA) + guides(colour = FALSE)
p = p + xlab("Group") + ylab("Mean FD")
p

# Hypothesis test
df2use = data2write
y_var = "meanFD"
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df2use, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   501 118.99740  <.0001
## subgrp          3   501   1.16334  0.3232
# Discovery
df4mod = subset(df2use,df2use$dataset=="Discovery" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   246 145.29571  <.0001
## subgrp          3   246   2.52924  0.0579
# Replication
df4mod = subset(df2use,df2use$dataset=="Replication" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   247 68.77640  <.0001
## subgrp          3   247  0.23437  0.8724

Plot ADOS scores in the subtypes and also run a hypothesis test

df2use = subset(data2write, data2write$subgrp!="TD")
df2use$ados_2_SA_CSS[df2use$ados_2_SA_CSS==999] = NA
df2use$ados_2_RRB_CSS[df2use$ados_2_RRB_CSS==999] = NA

df4plot = melt(df2use,
               id.vars = c("subid","subgrp"), 
               measure.vars = c("ados_2_SA_CSS","ados_2_RRB_CSS"))
df4plot$variable = as.character(df4plot$variable)
df4plot$variable[df4plot$variable=="ados_2_SA_CSS"] = "SA"
df4plot$variable[df4plot$variable=="ados_2_RRB_CSS"] = "RRB"
df4plot$variable = factor(df4plot$variable)

p = ggplot(data = df4plot, aes(x = variable, 
                               y = value, 
                               colour = subgrp, 
                               group = subid)) + facet_grid(. ~ subgrp)
p = p + geom_point(shape=1) + geom_line(alpha = 0.2) + guides(color=FALSE)
p = p + ylab("ADOS CSS") + xlab("ADOS Subscale")
ggsave(filename = file.path(plotpath, sprintf("final_EUAIMS_ADOS_jitterplot_z%s.pdf",as.character(z_thresh))))
p

# hypothsis test on ADOS SA CSS
y_var = "ados_2_SA_CSS"
df2use = subset(data2write, !is.element(data2write$subgrp,c("TD")))

# Hypothesis test
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df2use, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   260 9.643933  0.0021
## subgrp          2   260 0.801596  0.4497
# hypothsis test on ADOS RRB CSS
y_var = "ados_2_RRB_CSS"
df2use = subset(data2write, !is.element(data2write$subgrp,c("TD")))

# Hypothesis test
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df2use, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   260 9.855800  0.0019
## subgrp          2   260 0.780035  0.4595

Descriptive stats

df2use = data2write[,c("subid","Diagnosis","dataset","Centre","meanFD","age","sex","subgrp","viq_all","piq_all","fsiq4_all","ados_2_SA_CSS","ados_2_RRB_CSS","SRS_tscore","SRS_tscore_self")]
mask = df2use==999
df2use[mask] = NA
df2use$age = df2use$age/365

cols2use = c("dataset","subgrp","age","meanFD","viq_all","piq_all","fsiq4_all","ados_2_SA_CSS","ados_2_RRB_CSS","SRS_tscore","SRS_tscore_self")
res=describeBy(x = df2use[,cols2use], group = c("subgrp"))
res
## 
##  Descriptive statistics by group 
## group: RRB_over_SC
##                 vars  n   mean    sd median trimmed   mad   min    max range
## dataset*           1 16    NaN    NA     NA     NaN    NA   Inf   -Inf  -Inf
## subgrp*            2 16    NaN    NA     NA     NaN    NA   Inf   -Inf  -Inf
## age                3 16  16.81  5.64  16.52   16.93  8.19  7.89  24.03 16.14
## meanFD             4 16   0.24  0.25   0.18    0.18  0.07  0.06   1.14  1.09
## viq_all            5 16 100.54 17.69  97.50   99.47 15.57 73.00 143.00 70.00
## piq_all            6 16 102.05 18.21 100.50  101.49 14.08 64.00 148.00 84.00
## fsiq4_all          7 16 101.55 17.18 100.36  100.20 12.60 74.00 148.00 74.00
## ados_2_SA_CSS      8 16   4.44  2.83   3.50    4.36  3.71  1.00   9.00  8.00
## ados_2_RRB_CSS     9 16   3.69  3.32   1.00    3.50  0.00  1.00   9.00  8.00
## SRS_tscore        10 11  70.45 12.10  72.00   70.78  8.90 48.00  90.00 42.00
## SRS_tscore_self   11  6  61.17  6.52  63.50   61.17  3.71 49.00  67.00 18.00
##                  skew kurtosis   se
## dataset*           NA       NA   NA
## subgrp*            NA       NA   NA
## age             -0.10    -1.63 1.41
## meanFD           2.77     7.09 0.06
## viq_all          0.57    -0.07 4.42
## piq_all          0.45     0.89 4.55
## fsiq4_all        0.90     1.04 4.29
## ados_2_SA_CSS    0.21    -1.64 0.71
## ados_2_RRB_CSS   0.48    -1.64 0.83
## SRS_tscore      -0.16    -0.90 3.65
## SRS_tscore_self -0.92    -0.85 2.66
## ------------------------------------------------------------ 
## group: SC_equal_RRB
##                 vars   n   mean    sd median trimmed   mad   min    max range
## dataset*           1 106    NaN    NA     NA     NaN    NA   Inf   -Inf  -Inf
## subgrp*            2 106    NaN    NA     NA     NaN    NA   Inf   -Inf  -Inf
## age                3 106  16.40  6.07  15.42   16.02  6.04  7.08  30.28 23.20
## meanFD             4 106   0.26  0.39   0.19    0.21  0.12  0.04   3.95  3.91
## viq_all            5 104 100.32 16.86 102.33  100.76 15.83 64.00 136.00 72.00
## piq_all            6 104 102.96 18.38 105.30  103.61 18.83 61.00 142.00 81.00
## fsiq4_all          7 105 101.91 16.78 105.00  102.53 17.79 60.00 131.00 71.00
## ados_2_SA_CSS      8 104   5.84  2.58   6.00    5.88  2.97  1.00  10.00  9.00
## ados_2_RRB_CSS     9 104   5.02  2.63   5.00    5.02  2.97  1.00  10.00  9.00
## SRS_tscore        10  97  68.86 11.95  69.00   68.84 14.83 43.00  90.00 47.00
## SRS_tscore_self   11  54  61.41  9.80  61.50   61.25 11.12 43.00  89.00 46.00
##                  skew kurtosis   se
## dataset*           NA       NA   NA
## subgrp*            NA       NA   NA
## age              0.48    -0.68 0.59
## meanFD           7.80    69.70 0.04
## viq_all         -0.22    -0.65 1.65
## piq_all         -0.31    -0.58 1.80
## fsiq4_all       -0.33    -0.69 1.64
## ados_2_SA_CSS   -0.16    -1.05 0.25
## ados_2_RRB_CSS  -0.36    -0.87 0.26
## SRS_tscore      -0.02    -1.01 1.21
## SRS_tscore_self  0.18    -0.29 1.33
## ------------------------------------------------------------ 
## group: SC_over_RRB
##                 vars   n  mean    sd median trimmed   mad   min   max  range
## dataset*           1 144   NaN    NA     NA     NaN    NA   Inf  -Inf   -Inf
## subgrp*            2 144   NaN    NA     NA     NaN    NA   Inf  -Inf   -Inf
## age                3 144 16.32  5.03  15.99   16.17  5.61  7.48  29.4  21.92
## meanFD             4 144  0.25  0.27   0.16    0.19  0.11  0.03   1.6   1.56
## viq_all            5 140 96.94 19.58  99.00   97.40 20.74 50.91 142.0  91.09
## piq_all            6 142 98.49 22.24 102.49   99.66 21.31 44.03 150.0 105.97
## fsiq4_all          7 142 98.06 19.78 102.25   98.76 20.20 59.00 143.0  84.00
## ados_2_SA_CSS      8 138  6.31  2.64   7.00    6.44  2.97  1.00  10.0   9.00
## ados_2_RRB_CSS     9 138  4.70  2.75   5.00    4.62  2.97  1.00  10.0   9.00
## SRS_tscore        10 124 73.48 11.71  75.50   74.13 12.60 44.00  95.0  51.00
## SRS_tscore_self   11  63 63.10 10.88  61.00   62.27  8.90 40.00  94.0  54.00
##                  skew kurtosis   se
## dataset*           NA       NA   NA
## subgrp*            NA       NA   NA
## age              0.30    -0.54 0.42
## meanFD           2.95     9.71 0.02
## viq_all         -0.19    -0.54 1.65
## piq_all         -0.41    -0.51 1.87
## fsiq4_all       -0.31    -0.82 1.66
## ados_2_SA_CSS   -0.36    -0.92 0.22
## ados_2_RRB_CSS  -0.20    -1.14 0.23
## SRS_tscore      -0.38    -0.54 1.05
## SRS_tscore_self  0.68     0.55 1.37
## ------------------------------------------------------------ 
## group: TD
##                 vars   n   mean    sd median trimmed   mad   min    max  range
## dataset*           1 243    NaN    NA     NA     NaN    NA   Inf   -Inf   -Inf
## subgrp*            2 243    NaN    NA     NA     NaN    NA   Inf   -Inf   -Inf
## age                3 243  16.84  5.66  16.58   16.63  6.57  6.89  29.84  22.95
## meanFD             4 243   0.20  0.34   0.13    0.15  0.07  0.03   4.60   4.58
## viq_all            5 241 104.27 18.62 106.00  105.33 14.83 45.00 160.00 115.00
## piq_all            6 241 105.36 18.92 107.00  107.07 16.31 49.00 147.00  98.00
## fsiq4_all          7 241 105.33 17.70 108.18  107.16 13.61 50.00 142.00  92.00
## ados_2_SA_CSS      8   0    NaN    NA     NA     NaN    NA   Inf   -Inf   -Inf
## ados_2_RRB_CSS     9   0    NaN    NA     NA     NaN    NA   Inf   -Inf   -Inf
## SRS_tscore        10 133  47.54  9.34  44.00   45.82  4.45 37.00  90.00  53.00
## SRS_tscore_self   11 132  47.50  5.90  46.00   46.72  5.19 39.00  69.00  30.00
##                  skew kurtosis   se
## dataset*           NA       NA   NA
## subgrp*            NA       NA   NA
## age              0.28    -0.74 0.36
## meanFD           9.47   111.25 0.02
## viq_all         -0.55     0.92 1.20
## piq_all         -0.80     0.53 1.22
## fsiq4_all       -0.96     1.13 1.14
## ados_2_SA_CSS      NA       NA   NA
## ados_2_RRB_CSS     NA       NA   NA
## SRS_tscore       1.85     3.60 0.81
## SRS_tscore_self  1.18     1.39 0.51

Model age differences

y_var = "age"

# df4mod = subset(df2use,df2use$subgrp!="RRBoverSC")
df4mod = df2use
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   501 792.5869  <.0001
## subgrp          3   501   0.5403  0.6549
# Discovery
df4mod = subset(df2use,df2use$dataset=="Discovery" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   246 602.0249  <.0001
## subgrp          3   246   0.3993  0.7536
# Replication
df4mod = subset(df2use,df2use$dataset=="Replication" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   247 505.9907  <.0001
## subgrp          3   247   0.5217  0.6677

Model VIQ differences

y_var = "viq_all"

# df4mod = subset(df2use,df2use$subgrp!="RRBoverSC")
df4mod = df2use
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   493 1961.9135  <.0001
## subgrp          3   493    3.6635  0.0124
# Discovery
df4mod = subset(df2use,df2use$dataset=="Discovery" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   242 2229.0030  <.0001
## subgrp          3   242    2.0844  0.1029
# Replication
df4mod = subset(df2use,df2use$dataset=="Replication" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   243 1477.9386  <.0001
## subgrp          3   243    2.1664  0.0926

Model PIQ differences

y_var = "piq_all"

# df4mod = subset(df2use,df2use$subgrp!="RRBoverSC")
df4mod = df2use
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   495 1478.8200  <.0001
## subgrp          3   495    1.9176  0.1258
# Discovery
df4mod = subset(df2use,df2use$dataset=="Discovery" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF  F-value p-value
## (Intercept)     1   242 1885.666  <.0001
## subgrp          3   242    1.755  0.1564
# Replication
df4mod = subset(df2use,df2use$dataset=="Replication" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   245 1169.8593  <.0001
## subgrp          3   245    1.0994    0.35

Model FIQ differences

y_var = "fsiq4_all"

# df4mod = subset(df2use,df2use$subgrp!="RRBoverSC")
df4mod = df2use
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   496 2055.9457  <.0001
## subgrp          3   496    3.0199  0.0295
# Discovery
df4mod = subset(df2use,df2use$dataset=="Discovery" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   244 2758.4506  <.0001
## subgrp          3   244    2.3434  0.0737
# Replication
df4mod = subset(df2use,df2use$dataset=="Replication" & df2use$subgrp!="RRBoverSC")
# construct linear model
# mixed-effect model: site as random factor, all other covariates as fixed factors
fx_form = as.formula(sprintf("%s ~ %s",y_var,"subgrp"))
rx_form = as.formula(sprintf("~ 1|%s","Centre"))
mod2use = eval(substitute(lme(fixed = fx_form, random = rx_form, data = df4mod, na.action = na.omit)))

# run ANOVA
res = anova(mod2use)
res
##             numDF denDF   F-value p-value
## (Intercept)     1   244 1450.1789  <.0001
## subgrp          3   244    1.5459  0.2032